Introduction
Visualizing statistics associated with taxa can be difficult due to the hierarchical nature of the information. Traditional graph types used to visualize the relationship between categories (e.g. taxa) and quantities (e.g. abundance) such as bar charts, pie charts, and box plots, are fundamentally two-dimensional. This means the it is usually necessary to only view a ‘slice’ of the data, such as the abundance of taxa of a particular rank, rather than all ranks. To demonstrate this idea, lets display the same data using various graphing techniques and compare their effectiveness. For this example we will be using a sample of 500 sequences from the UNITE fungal ITS database. First, we will use a bar chart to display abundance of taxa at each rank:
For ranks with relatively few taxa, this is a satisfactory graphing technique, but it is ineffective once there is more than 20 or so taxa. It also is difficult to discern how sub-taxa are distributed within each taxon. For example, if we only looked at the phylum or class level, we can easily see that Basidiomycota/Agaricomyctes are the most plentiful, but don’t know if that is due to a single highly abundant species, or many moderately abundant species. These details are typically important for the interpretation of results.
Pie charts are also commonly used for this purpose, but they are just a less effective version of a bar chart:
Metacoder approches this problem by using size and color to represent numeric data distributed along a phylogenetic tree:
In this example, both size and color are being used to represent the abundance of taxa. Using this method, it is clear how child taxa are distributed within their parents and what taxa are unusual (note the unidentified agaricales).
Basic usage
Although there are many options that can be used to make highly customized graphs, plot_taxonomy only needs two arguments to function. These are a list of unique taxon IDs and a corresponding list of IDs of parent taxa (i.e. one rank lower). These two lists effectively constitute an adjacency/edge list, describing how taxa are connected in a phylogenetic tree. We can see the default appearance of the data used in the introduction using the code below. The taxa object is one of the two data.frames returned by the function extract_taxonomy, which is covered in another vignette.
head(taxa)
## rank name taxon_id parent_id item_count level
## 1 Kingdom Fungi 1 <NA> 500 1
## 2 Phylum Ascomycota 2 1 180 2
## 3 Class Leotiomycetes 3 2 32 3
## 4 Order Helotiales 4 3 28 4
## 5 Family Hyaloscyphaceae 5 4 25 5
## 6 Genus Lachnum 6 5 17 6
plot_taxonomy(taxa$taxon_id, taxa$parent_id)
Each vertex (i.e. circle) in the graph represents a taxon and each line represents its membership in a lower taxon.
Vertex/Line size
By default, the size of each vertex is inversely proportional to its rank. This makes higher ranked taxa smaller, making them easier to see. The size of vertices and lines can be scaled to a number associated with each taxon using the size parameter. Below, the number of sequences for each taxon is used to determine vertex size.
plot_taxonomy(taxa$taxon_id, taxa$parent_id,
size = taxa$item_count)
Note that it was not necessary to specify the absolute vertex size; the range of absolute vertex sizes is optimized for each graph so as to minimize overlap of vertices and maximize the ranges of sizes. The argument overlap_bias is used to determine how much overlaps are avoided. Higher values mean more importance is given to avoiding overlapping vertices than to maximizing the ranges of sizes. A high overlap_bias makes the connections between taxa more clear, but diminishes the visual effect of vertex size.
plot_taxonomy(taxa$taxon_id, taxa$parent_id,
size = taxa$item_count,
overlap_bias = 50)
To low of an overlap_bias can make the graph hard to read:
plot_taxonomy(taxa$taxon_id, taxa$parent_id,
size = taxa$item_count,
overlap_bias = 1)
Vertex color
The vertex_color argument works in a similar way to size. Numeric values are translated to a range of colors. Below the abundance of samples for each taxon is used to determine color instead of size.
plot_taxonomy(taxa$taxon_id, taxa$parent_id,
vertex_color = taxa$item_count)
The range of color used can be set using the vertex_color_series argument. This argument take a list of colors in the form of names, hex color codes, or integers.
plot_taxonomy(taxa$taxon_id, taxa$parent_id,
vertex_color = taxa$item_count,
vertex_color_series = c("turquoise", "darkorange3", "#4e567d", "gold", 23))
Line color
Unlike size, the color of lines can be set independently of vertices, although the default behavior is for the lines to have the same color as the vertices. To only color vertices, you can set the lines to be a constant color:
plot_taxonomy(taxa$taxon_id, taxa$parent_id,
vertex_color = taxa$item_count,
line_color = "grey")
Or vise-versa:
plot_taxonomy(taxa$taxon_id, taxa$parent_id,
vertex_color = "grey",
line_color = taxa$item_count)
